A Novel, Interdisciplinary, Approach for Community Detection Based on Remote File Requests

Community structures are formed in many real-world networks, e.g., biological or medical groups, student groups, and so on. Communities are perhaps the most important feature of today's networks, since the majority of people who join a network also tend to join one or more communities. Therefore, several researchers find that the detection of hidden communities is a very interesting and challenging research field. Communities are represented as the groups of nodes on a graph, corresponding to users with similar interests. This paper introduces a novel, interdisciplinary, approach for community detection, combining social networks and distributed systems, where remote access to shared files is offered in a networked environment. A new metric, based on data requests, is introduced and used as a measure of the belonging degree of a node in a certain formed community. Two sets of simulations are used to verify our scheme: simulation results on synthetic networks and results derived from real data.